Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

This is what the model configuration looks like for our classification problem:
Model Configuration
1 # Sets learning rate - this is "eta" ~ the "n"-like Greek letter
2 lr = 0.1
3
4 torch.manual_seed(42)
5 model = nn.Sequential()
6 model.add_module('linear', nn.Linear(2, 1))
7
8 # Defines an SGD optimizer to update the parameters
9 optimizer = optim.SGD(model.parameters(), lr=lr)
10
11 # Defines a BCE with logits loss function
12 loss_fn = nn.BCEWithLogitsLoss()
Model Training
Time to train our model! We can leverage the StepByStep class we built in Chapter
2.1 and use pretty much the same code as before:
Model Training
1 n_epochs = 100
2
3 sbs = StepByStep(model, loss_fn, optimizer)
4 sbs.set_loaders(train_loader, val_loader)
5 sbs.train(n_epochs)
fig = sbs.plot_losses()
232 | Chapter 3: A Simple Classification Problem